.png){kind=icon}
Return
Quantum Neural Network With Parallel Training for Wireless Resource Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2023.3321467.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of the training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.
Keywords:
Training
Wireless communication
Optimization
NOMA
Precoding
Transmitting antennas
Qubit
Quantum neural networks
unsupervised learning
non-orthogonal multiple access
Journal
IF:
9.2
Papers:
5.8K
Citations:
1.8W
Organization
Cited Papers
ShareSave
Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveElectrolyte Effects on Electrochemical Properties of Osmium Complex Polymer Modified Electrodes
PREAI
ShareSaveShareSaveShareSave
View more